Surveillance security systems have been traditionally used to help protect people, property, and reduce crime for homeowners and businesses alike and have become an increasingly cost-effective tool to reduce risk. These security systems are used to monitor buildings, lobbies, entries/exits, and secure areas within the buildings, to list a few examples. The security systems also identify illegal activity such as theft or trespassing, in examples.
In these surveillance security systems, surveillance cameras capture image data of scenes. The image data is typically represented as two-dimensional arrays of pixels. The cameras include the image data within streams, and users of the system such as security personnel view the streams on display devices such as video monitors. The image data is also typically stored to a video management system for later access and analysis.
Modern surveillance security systems also often include image analytics systems that analyze the image data for objects of interest and behaviors of interest. These image analytics systems might execute both real-time analysis of image data sent directly from the surveillance cameras, and forensic analysis of stored image data. In many instances, these image analytics systems analyze the image data to detect, identify, and track objects of interest such as individuals and vehicles, and can spot trends and search for specific behaviors of interest. The image analytics systems can also send alert messages to users of the system upon identifying the objects of interest/behaviors of interest.
As part of operation, typical image analytics systems create a background model of the scene, and analyze the image data relative to the background model to identify objects. To create the background model, one or more cameras first capture image data of the scene over a continuous time period, such as five minutes. Then, the image analytics systems create the background model from static portions of the image data and store the background model. The systems then apply pixel-level analysis algorithms to the image data and the background model to identify the objects. Using a foreground segmentation algorithm, for example, the image analytics systems compare the pixels of the image data against that of the background model to determine whether the pixels in the image data are background or foreground pixels. Regions of related foreground pixels are then identified as foreground objects.
The image analytics systems might then track the foreground objects in the image data of the scene over time. Foreground objects of interest include individuals, vehicles, luggage, and goods, in examples. To track the foreground objects, the image analytics systems create bounding boxes for each of the foreground objects. The bounding boxes are notionally superimposed upon the foreground objects within the image data and stored as metadata. The systems track motion of the foreground objects via the bounding boxes, and sent alerts to users/operators in response to identifying and tracking the foreground objects.